# I would like you to help me write a research request for Google Gemini Deep Rese ## Retrieval Notes - Session id: `1f6db0b3-b429-44ba-a503-01786dea3c53` - Last activity: `2026-01-31T18:22:50.287Z` ## My Notes <!-- Add your notes here. This section is preserved across syncs. --> ## Conversation ### User I would like you to help me write a research request for Google Gemini Deep Research. I think you have a skill about this, I can't remember. But yeah, what I want you to request is a report explaining how base models have got more intelligent over time. I'm talking about LLMs, of course. So, what is it about ChatGBT5 versus ChatGBT4, okay, in the non-reasoning incarnation of both? What makes one system smarter than the other? And what I'm looking for here is something that makes reference to... Is it information being extracted from the data or the data set being better or a mix? What fact... How is... Yeah. What is the... Yeah. Why is ChatGBT5 more intelligent than ChatGBT4? Okay. Or why is Claude 4.5 Opus smarter than Claude 4.5 Sonnet? Okay. To choose two very recent models. One of them is smarter than the other, I'm talking about the non-reasoning variants of all these models. ### Assistant Looking at the available skills, I don't see a specific Gemini Deep Research skill. But I can help you craft a good research request directly. Here's a structured prompt for Gemini Deep Research: --- ## Research Request: Sources of Intelligence Gains in Base LLMs **Question:** What technical factors explain why newer/larger base language models (non-reasoning variants) outperform their predecessors on benchmarks and real-world tasks? **Specific comparisons of interest:** - GPT-5 vs GPT-4 (base models, not o1/o3 reasoning variants) - Claude 4.5 Opus vs Claude 4.5 Sonnet - Llama 3.1 405B vs Llama 3.1 70B **I want the report to address these potential factors:** 1. **Scale** — Does simply increasing parameters extract more "latent knowledge" from the same data? What's the evidence for/against scaling laws as the primary driver? 2. **Training data quality** — Are newer models trained on better-curated datasets? What role does data filtering, deduplication, and quality thresholds play? 3. **Training data quantity** — How much comes from training on more tokens? Are there diminishing returns? 4. **Architecture improvements** — What architectural changes (attention mechanisms, positional encoding, etc.) contribute to capability gains independent of scale? 5. **Training techniques** — What role do improved optimizers, learning rate schedules, curriculum learning, or other training innovations play? 6. **Post-training** — How much of the apparent intelligence gain comes from RLHF/RLAIF, instruction tuning, or other post-pretraining processes vs. the base model itself? **What I'm trying to understand:** Is a smarter model primarily extracting more information from the same data (better compression/representation), or is it learning from better/more data, or is it architectural improvements that enable better reasoning patterns? I want to understand the relative contribution of each factor based on published research and technical reports. **Preferred sources:** Technical papers, model cards, scaling laws research (Hoffmann et al., Kaplan et al.), and official technical reports from Anthropic, OpenAI, Meta. --- Want me to adjust the framing or add any specific angles? I could also tighten this if Gemini Deep Research has a character limit. *I modelli più recenti estraggono davvero più conoscenza dagli stessi dati, o è solo una questione di scala?* ### User you really don't have a skill? we had a long conversation about the flaws in a report and worked out a prompting technique to minimise them... ### Assistant You're right, let me search for that. We clearly worked on this together and I should have that captured somewhere. ### Assistant Found it! It's in my knowledge base. Let me read it: ### Assistant There it is! We did that work together. Here's the revised research request using the prompting technique we developed: --- ## Research Request: Sources of Intelligence Gains in Base LLMs **Question:** What technical factors explain why newer/larger base language models (non-reasoning variants) outperform their predecessors? Specifically, what's the relative contribution of: (1) scale/parameters, (2) training data quantity, (3) training data quality, (4) architecture changes, (5) training techniques, and (6) post-training methods? **Comparisons of interest:** - GPT-5 vs GPT-4 (base, not o-series reasoning models) - Claude 4.5 Opus vs Claude 4.5 Sonnet - Llama 3.1 405B vs Llama 3.1 70B **Core question:** Does a smarter model primarily extract more knowledge from the same data (better compression/representation), or learn from better/more data, or benefit from architectural innovations? What does published research say about the relative contribution of each factor? **VERIFICATION REQUIREMENTS:** - For all quantitative claims: cite exact source with URL and page/table number - For framework descriptions: quote the source's exact terminology - For paper summaries: include direct quotations showing the argument - For benchmark results: state what the benchmark measures according to its creators - Distinguish: (a) what sources say, (b) standard interpretations, (c) your inferences - Flag any claims you cannot verify with direct quotation - Do not use citation-style formatting without actual URLs **Key sources to consult:** Chinchilla scaling laws (Hoffmann et al.), Kaplan et al. scaling laws, model technical reports from Anthropic/OpenAI/Meta, and any ablation studies separating these factors. --- That verification block should help guard against the failure modes we documented. Want me to tweak anything? *Mi scuso per aver dimenticato—dovrei consultare la mia base di conoscenza prima di dichiarare che qualcosa non esiste.*